{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = './run/tables/deeplab-resnet/experiment_4/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_folder = os.path.join(data_path, 'train')\n",
    "val_folder = os.path.join(data_path, 'val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "acc_list = [[], [], []]\n",
    "recall_list = [[], [], []]\n",
    "\n",
    "for i in np.arange(10):\n",
    "    fn = \"cm_{0}.csv\".format(i)\n",
    "    cm = pd.read_csv(os.path.join(train_folder, fn), index_col=0, header=0)\n",
    "    true_val = np.sum(cm, axis=1)\n",
    "    det_val = np.sum(cm, axis=0)\n",
    "    for c_i in [0, 1, 2]:\n",
    "        acc = cm.iloc[c_i, c_i] / true_val[c_i]\n",
    "        recall = cm.iloc[c_i, c_i] / det_val[c_i]\n",
    "        acc_list[c_i].append(acc)\n",
    "        recall_list[c_i].append(recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "line_style = ['dotted', 'dashed', 'dashdot']\n",
    "markers = ['o', 's', '*']\n",
    "labels = ['lines', 'text', 'background']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efe1ff53cd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for i in np.arange(len(acc_list)):\n",
    "    plt.plot(acc_list[i], linestyle=line_style[i], marker=markers[i], label=labels[i])\n",
    "plt.ylim(0, 1)\n",
    "plt.xlim(0, 9)\n",
    "plt.grid()\n",
    "plt.xlabel('Epoches')\n",
    "plt.ylabel('Training Accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efe1f640ee0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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57uWvWEnhyHaYe0/peeKA5udZjf7xvbD+fauBsV9OyLeOH1lxBeBKhrqtfQ2Sb1lHFES5rPkN2kOX0cUNlr+Of37ahdaeub2ss3hdYO0Vt+hjle1tRMFbv604pnGfFr8f9rC1V12yUXcWnzTA5S9bycpu1JylG/fr5vgawwqOhf7+89Ll+wMc6P7DtydPu/q9iusDXPZCxfMcThj2UMXzo13Q/0+Bk0K331U8z5Vk/Q0Hmt/ivIrnx8RDvYp3Xk9VtUsKglDkLaJN7Tb2+eyqhKrYK/ePobqPW3usyc2s6ZvnwIk91nT3MetVt03gdb3/e6vBAHi2NxRkl57f/VqrYRaBT/5MqQbdGWt9YdsNt34e2W41gtFx4EqBxDiIq2PVjU2CXr+H6FrWl3j+AxXHdM374Ii2GsW6ba1pDTrAhG9LN+iOKKjlOxjesh/cnVE8X5xWA+iXfhHcuaPibaYHPuhO2oXFQzXlaTPEelWk5fnFe7bladLNep2KRp2K3yc3DVw3LiXw/Ai7eVyNEd/glIcfq11SaBTdiFHtRkXMUE3ECWZc2lNoNdz5x6yhlAa+bvKWeVYXPb9Eo+5KgWH/tua/dw3s/Naa7/VdNNSsF1zvuzBm/gNwYIP13hFl7d22+UXgeNtfXPz+4setxt/fsEe5IKmJNU8EbttoTfO/SjYk0XFw0xIq5EqCof8qLgdKCuUdoIupVboRLMsZXXrPuKaoqFGJb1D1sZTdfqTFFYkxldgRXDFJVgSzSLVLCtESzT197qm84tmiKN8aJwXYsypw3U9ugzXvWHv5fvH1rXFngOUvwWbfgfSYRKshrV/iHlpNult/4K5ka54r2RoP9bv6XWsP25VsNdL+oYB10yqOqVOJ4YkuVwaO358gaqoIb1QWLlzIoEGDwhdLSZEYVyTGdBqqXVI4K3i9kHsYsvdbr9T+VsO/+TNYNx2yDxTPcx+Dv+219mTXzQi83ua9fUMrydZwiivZOiXO75JnrL3v2KTyD+ZdcFvg9adE1nMuAtIGWKlyaVI4Xaczdl/ohuOZ1nnF2fuLG/de11tjsmunWwcrcw5aY/V+f1xpjc0fy7B6AwkNrSGf1oOs85j9p+j1uxWWVHx/GbpcZb0qEl+34nk/hzbASlUbmhROV6Cx+68eLd6Tzz4AFz5gndmxZe7JZ7CI0zqAmNzUeqVdaDX6CQ2tBj+hoXUuOVin5/lP0StPQpjHeSuiDbBS1YYmhfJ4CiHnkNWo5xy0TjmslwbH91h78tmVHM1f8C/r9D9/w+7fk2/aA0b+t7ixT2honR3jP2Ba2RkiwYjEvXKlVLVR7ZJC4omtxecon+ppll6vdfVf9gGr4cz2vVqcZ10ZmHsEnulpjeeXNPjvMPCv1vnV/uGbQO45UHzwt6TkpoGHb84E3StXSv0M1S4plJJzAE7sB29h8bny3z1vjb2XbPjbDLFORxSB/w2x6vtFx1vnrXccYR147Xipb0++gZV0EhpCnVZW3cRGcIvvDJ9AF8+UlxCUUqoaqN5JAeCxdKvRH/OhVf7uOes+Lv6GvU5ra/gHrKRw1dtW459Q32r0Y0vcIM7hhF+XcysApZQ6S1T/pPCrKcVXnQLc9H3pc+TLSr+o/OmnSsfulVI1UPVPCmXPxok5s/fcr5CO3SulaiC94YhSSilb9U4KOlSjlFJnVLUbPjqR2Bbu3xzuMJRSqkaq3j0FpZRSZ5QmBaWUUjZNCkoppWyaFJRSStk0KSillLJpUlBKKWXTpKCUUsqmSUEppZRNk4JSSimbJgWllFI2TQpKKaVsmhSUUkrZNCkopZSyhTQpiMgwEdksIltF5K5y5ieLyCwRWSMiG0TkulDGo5RSKrCQJQURcQLPAsOBjsBoEelYptrNwEZjTBdgEPCYiMSEKiallFKBhbKn0BvYaozZbowpAN4FRpSpY4BEEREgATgCFIUwJqWUUgGIMSY0Kxa5HBhmjLneVx4DnGeMmViiTiLwMdAeSASuNMZ8Ws66xgPjAerXr99j2rRpIYn5dGVnZ5OQkBDuME4SiXFpTMHRmIIXiXFFYkyDBw9eYYzpWWlFY0xIXsAVwIslymOAp8vUuRx4AhCgLbADSAq03vT0dBNpFixYEO4QyhWJcWlMwdGYgheJcUViTMByE0TbHcrhowygeYlyM2BPmTrXAR/4Yt7qSwrtQxiTUkqpAEKZFJYBaSLSynfw+CqsoaKSdgO/ABCRhkA7YHsIY1JKKRVAVKhWbIwpEpGJwOeAE3jZGLNBRCb45k8F/gm8KiLrsIaQ7jTGHApVTEoppQILWVIAMMbMBmaXmTa1xPs9wEWhjEEppVTw9IpmpZRSNk0KSimlbJoUlFJK2TQpKKWUsmlSUEopZdOkoJRSyqZJQSmllE2TglJKKVtIL15TSp2amasymfz5ZjKz8mj63ZfcMbQdl3ZrqjFVk7giOaaYRm17BFNfk4I6a0XaF3jmqkzu/mAdeYUeADKz8rj7g3UAYYsrEmOqqrgKPV4Aop3WgEpOfhEOEeJinAAczSnA6RSSXNEAvLZ4Bw999gPuQq8d010frLVjyszKIyEmiuRaVv09WXnEx0aRHGeV9x7LIyE2ikRXNMYYDpzIJz42ioTYKLxew6FsqxzvKx/OKSAhNoq4GCcer+ForlV2RVvlY3mFzN+0n3s/2mD/noKhw0eqSsxclUm/h79k3Jwc+j38JTNXZYY9nrs/WEdmVh5Q3KgEissYQ6HHi8dr7PJxdyFu3xfO4zXsP+4mJ996TlShx8vOQzkcdxcCkF/kYcOeY2TlFgCQV+Bhxa4jHMmxyo/M+eGkL29eoYeH5/wAwJGcAj7fsI/D2fkAHDyRz6dr99rL7z/u5qPVmRz1lTOz8pixIsPe3u7Duby3bDfH8qx4th/M5s3vdnHCF9+P+0/w6rc77Pg37DnGfR+vLzemyZ9vZumOIzw+dzNFvsbzmy0HefCTjf7b4vPFxv3c+9F6e7mP1+zhrvfX2uVpy37iL9PW2OXXFu/klndW2eXnFm7lprdW2OXH5m5m/OvLAZj8+eZy4yq5/lveWcX/vbrMLl//2nKue2WpXR79wnel5o94ZlGp8m+eXsTEt1cWl59ZxB0z1pQq3//xBrs8adZGOyH4uQu9TP58MwBDn/iap77cYs8bNGUhzy/cZpfPf/hL/ve1dT/QIq/hvH/P59Vvd1jrKfLQ+9/zefO7XQAcdxfS61/zeHfZbgAO5+TT88F5zFiRAcC+4266//ML/vXpplNKCKBJoUaqzg1wkcdrN7IAGUdz2eNbDmDFriNs2HPMLn+8Zg/fbi2+h+LzC7cxe91eu/z3D9cxbdlPdnnMS9/z0qIdFTYqf562mmd8X9wij5e2f5ttl/MKPaT9/TNe/Mb64h7PK6Lz/XN5+/viL+Z5/57Ph77PdeBEPoOmLOQzXzx7s9xc/NQiFmw+AMBPR3P57fNLWLzNin/fMXe5vz//9C37T3DjGyv4Yd8JAH7Yd5yb317JtoPZgNWI3/ruanYdyQVgXcYxbp++hj1Z1vKrM7K48/11HDxhlVftzuKemevtpLJi11Hun7XRTmIrdh3lWF75D0Lck5XHil1HeXrBVgo9VhJYl3mMd5butpPmj/tPMGf9PnuZn47ksnzXUbt8MDufHYey7XJOQRFZvoQFIAjWQxktia4oateKsbdfHndRcaPctXkKPVrWtsv929blgrT6dnn4uY24sGNDu3xFz+b8unNjuzz2/FRGdC3udUwY2IaRJXoht12Yzm+7N7PL3gqeV+aP9Z+XnlNq/f+6tBPDOzWyyw+NPJdf+uJxivDvkecyqF0DwOqt/GtkJ/qn1QPAFe3kn5d2ok/rutbvJjaaf444h16pdQBIjotm0iXn2DsApyJkT14LlXbt2pnNmzeHOwygzPBDSlzYhx/8MZXsVgPERTt56LJz7diMMfaXLSe/iJyCIhokugCrATqSU0DHJkmA9cU+cDzf/mNctvMI+465+U2XJgDM3bCP/cfdjOmbCsB7y3Zz8EQ+E4ekAfDsgq08v3Ar2fkn7600TYnj3KbJeI3hhWutB0Jd+uy3JMVF8/r/9Qbg4qe+oXGyixfH9gLgoie+ok39BJ6/xhoeHTxlIec2Teap0d0AGPDoAs5vU5eHf9sZgJHPfcsFafW57cJ0AMa/vpwL0upx70cbqOgv/5VxvRjcvgFer+GxLzbTr209zm9Tj0KPl/9+tY2+berRo2Vt8os8vLFkF31a16VT02TchR4+WJlJr9TapDVMJLegiM837KNb89qk1osnJ7+Ib7YconOzZJqkxJGTX8TyXUfp0DiRBoku+j40n73lJIbGyS6W3P0LcvKL2HEoh9R68STERpGTX0TG0Tya14mjVoxV3nfcTdOUOFzRTnILijh0ooCGybHERjnJK/BwNLeA+omxRDsd5BV4OOEupE58DFFOB+5CD7kFHpLjonE6hPwiD4MmLyw3pqYpcSy6c3CpRrsq9Xv4S3sno6SmKXF8e9eQMEQU+THtfe1P5O/dUuk/mPYUTlNFe7/vLd3N+sxjZPu64EdzCvhmy0F772vfMauLfyzXKu88lMNri3faXfxNe4/zxBc/2uUVu45y70fr7frfbDnIxLdX2uubs34fv3vxO7vLf/+sk8cP8wo9PPq5NQTx+NzNtP37Z/a8/8zfwsBHF9rlqV9t48oXltjl1xbv5E/vFXfppy//iX/P3mSXP1u/jxe+KX4ExvKdR1mw+aBdPnDcXW5CAGsPqkfL2vRMLd6bG9OnJZf3KN77umNoO264oLVdfnxUV+4Y2s4uv3djHx4c2ckuf/3XwXZCAPjwpn52QgB44dqejOmbSpOUuHJjapoSx+D21t6ZwyHcMbQ957exEmK008HEIWn23mdslJPrL2hNp6bJgLX3dvV5LUhrmAhArZgoRnZrRmq9eADiY6MY1qmRve342CgGpte3E/Kdw9oTF+0sFU9ctJM7h7W363dqmkxCbJRdbtcokVoxxeU29RNw+dZRKyaKFnVrERtlleNinDRJibPHyONinDRIchHlK7uindSJj8HpEPvzVRTTHUPbhS0hgPV3UVFc4VJdYqqMHmg+TRUNP0yeu5lD2QW8O74PfVrXZU1GFuNeWcb7fzifHi1rsy7T6uLPmtifc2sls3Hvce77eAN9WtclpVYMP+4/wX/mb2FE1yak1IrhpyO5fLxmDxMGtiG5VjRHcgrYuOc4Bb5ucpHXi7vQi8fX48vKLb+7uNc3hHBe67qIiN1bGHpOI1r5Gi2AK3s1Z0B6Pbt844A2jO7dwi7fNbwDf7mouIv+6OWdcZRoHCZf0aXUdieN6MS8TQfK3YNqkhLHDQNal5r22xIJAbC7z37+BtjP36CeqjuGtiu3RxXOL7C/JxdJvc9IjClS44r0mPZWUtdPh49OUUGRlyfm/VjqAFFJAkwd04NeqXWoEx/DsdxCfjxwgvaNEkl0RZOdX8S+Y26a1ba6+P4ue5IriiinA6/XIMJp74VFYhc2mCGtcMUVSV/gkhYuXMigQYPCHUYpkRgTRGZckRiTiKwwxvSsrJ72FIJgjCEzK49mtWsR7RQWbTlEQmyUPURUUpOUOIaeU3zwKLlWtH3wByAhNoq2DRLssivaaXf3wRqy+Dl0D/jU4rq0W9OI/AIrFS6aFIJwz8z1fL5hP4vvGkJMlIP3/3A+s9ftjbjGF7QBVkr9PJoUypGZlcf/vt7On36ZRkqtGC7r3pTuLWrjH9GJiXJEbOML2gArpU6fJgUfj9eQV+ghITaKE+5C3v5+NwPS6zGkfUN6tKxDj5Z1TlpGG1+lVE2jp6RiXXn6y8e/4rG51gHs9o2SWHbPLxnSvmElSyqlVM1y1iaF9ZnHeH3JTsA6//yKns3o37b4VEz//UiUUupsclYNHxV6vPaFO7PW7OGdpbv5bfdmxMdGcdOgtmGOTimlwu+s6Sms3H2Uvg/NZ21GFmDdx2TRXUOIjz2r8qJSSgVUY1tEYwzf7ziCK9pJ1+YptG2QQK/UOkQ5rDxYOz4mzBEqpVTkqTApiMgsqPCeYRhjLglJRD+T//YNHq/hz++tpnOzZP47pidJrmj7JmpKKaXKF6inMKXKojhDXv12B7PW7mXGhL5EOR28NLYXrevHV76gUkopIEBSMMZ8VZWBBGvncS/9HraekvWbLk34+seD9G1TF1e0k5RaMTRNiSM7v4hEV7R9+2ellFLBCTR8tI7Aw0edK5oXav7bVG85cIJnF2zjP1d1ZUTXpvbFZEoppU5PoOGjX1dZFKchr9DDzFWZ/O/angxqV7/yBZRSSlUq0PDRrqoM5HTsyXKXepyeUkqpn6fS6xREpI+ILBORbBEpEBGPiByviuAqU9HTs5RSSp2eYC5eewYYDWwB4oDrgadDGVQwIuE21UopVdMEdfGaMWariDiNMR7gFRFZHOK4Aoqk21QrpVRNEkxSyBWRGGC1iDwK7AWCOvlfRIYB/wGcwIvGmIfLqTMIeBKIBg4ZYwYGWmdqkiNsj5VUSqmaLpjhozG+ehOBHKA58NvKFhIRJ/AsMBzoCIwWkY5l6qQAzwGXGGPOAa44leCVUkqdWcH0FA4BBcYYNzDJ19jHBrFcb2CrMWY7gIi8C4wANpaoczXwgTFmN4Ax5sCpBK+UUurMEmMqvD7NqiDyHfBLY0y2r5wAzDXGnF/JcpcDw4wx1/vKY4DzjDETS9R5EmvY6BwgEfiPMeb1ctY1HhgPUL9+/R7Tpk0L+gNWhezsbBISEsIdxkkiMS6NKTgaU/AiMa5IjGnw4MErjDE9K61ojAn4AlYHM62cOldgHUfwl8cAT5ep8wzwHdYxinpYZzilB1pvenq6iTQLFiwIdwjlisS4NKbgaEzBi8S4IjEmYLmppN02xgR1TCFHRLr7CyLSA8gLYrkMrOMPfs2APeXUmWOMyTHGHAK+BroEsW6llFIhEMwxhT8B00XE36A3Bq4MYrllQJqItAIygauwjiGU9BHwjIhEATHAecATQaxbKaVUCFSaFIwxy0SkPdAOEOAHY0xhEMsVichE4HOsU1JfNsZsEJEJvvlTjTGbRGQOsBbwYg03rf8Zn0cppdTPUGlSEJFawG1AS2PMDSKSJiLtjDGfVLasMWY2MLvMtKllypOByacWtlJKqVAI5pjCK0AB0NdXzgAeDFlESimlwiaYpNDGGPMoUAhgjMnDGkZSSilVwwSTFApEJA7fA3dEpA2QH9KolFJKhUUwZx/dB8wBmovIW0A/YFwog1JKKRUewZx99IWIrAT6YA0b3UqQN8RTSilVvQQcPhKRvr7bVTiNMZ8Cu4GngEVVEZxSSqmqVWFSEJHJwMtYd0T9VETuA74AvgfSqiY8pZRSVSnQ8NHFQDdjjFtEamPdoqKzMWZL1YSmlFKqqgUaPsoz1u2yMcYcBTZrQlBKqZotUE+hjYh8XKKcWrJsjLkkdGEppZQKh0BJYUSZ8mOhDEQppVT4VZgUjDFfVWUgSimlwi+YK5qVUkqdJTQpKKWUsmlSUEopZQvmeQqz8N0Mr4RjwHLgv/7TVpVSSlV/wfQUtgPZwP98r+PAfiDdV1ZKKVVDBHOX1G7GmAElyrNE5GtjzAAR2RCqwJRSSlW9YHoK9UWkhb/ge1/PVywISVRKKaXCIpiewl+ARSKyDevW2a2Am0QkHngtlMEppZSqWsE8T2G2iKQB7bGSwg8lDi4/GcLYlFJKVbFgegoAPYBUX/3OIoIx5vWQRaWUUiosgjkl9Q2gDbAa8PgmG0CTglJK1TDB9BR6Ah2NMWWvVVBKKVXDBHP20XqgUagDUUopFX7B9BTqARtFZCmQ75+oz1NQSqmaJ5ikcH+og1BKKRUZgjklVZ+roJRSZ4kKk4KILDLG9BeRE5S+IZ4AxhiTFPLolFJKValAT17r7/uZWHXhKKWUCqegLl4TESfQsGR9Y8zuUAWllFIqPIK5eO2PwH1Yt8v2+iYboHMI41JKKRUGwfQUbgXaGWMOhzoYpZRS4RXMxWs/YT1pTSmlVA0XTE9hO7BQRD6l9MVrj4csKqWUUmERTE9hN/AFEAMklnhVSkSGichmEdkqIncFqNdLRDwicnkw61VKKRUawVy8Nul0Vuw7Y+lZ4EIgA1gmIh8bYzaWU+8R4PPT2Y5SSqkzJ9DFa08aY/4kIrMoffEaENS9j3oDW40x233rexcYAWwsU++PwPtAr1MJXCml1JknFd0RW0R6GGNWiMjA8uZXdvsL31DQMGPM9b7yGOA8Y8zEEnWaAm8DQ4CXgE+MMTPKWdd4YDxA/fr1e0ybNi2Yz1ZlsrOzSUhICHcYJ4nEuDSm4GhMwYvEuCIxpsGDB68wxvSstKIxJiQv4ArgxRLlMcDTZepMB/r43r8KXF7ZetPT002kWbBgQbhDKFckxqUxBUdjCl4kxhWJMQHLTRBtdzAXr6UBDwEdAVeJZNK6kkUzgOYlys2APWXq9ATeFRGwbtH9KxEpMsbMrCwupZRSZ14wp6S+gnVF8xPAYOA6rJviVWYZkCYirYBM4Crg6pIVjDGt/O9F5FWs4aOZwQSulFLqzAvmlNQ4Y8x8rOMPu4wx92MdAwjIGFMETMQ6q2gTMM0Ys0FEJojIhJ8TtFJKqdAIpqfgFhEHsEVEJmLt9TcIZuXGmNnA7DLTplZQd1ww61RKKRU6wfQU/gTUAm4BegDXAGNDGJNSSqkwCdhT8F1YNsoYcweQjXU8QSmlVA1VYU9BRKKMMR6gh/hOD1JKKVWzBeopLAW6A6uAj0RkOpDjn2mM+SDEsSmllKpiwRxorgMcxjrjyOB7RjOgSUEppWqYQEmhgYjcBqynOBn4lX9vDKWUUtVaoKTgBBIo/0I1TQpKKVUDBUoKe40xD1RZJEoppcIu0HUKesaRUkqdZQIlhV9UWRRKKaUiQoVJwRhzpCoDUUopFX7B3OZCKaXUWUKTglJKKZsmBaWUUjZNCkoppWyaFJRSStk0KSillLJpUlBKKWXTpKCUUsqmSUEppZRNk4JSSimbJgWllFI2TQpKKaVsmhSUUkrZNCkopZSyaVJQSill06SglFLKpklBKaWULSrcAZwJhYWFZGRk4Ha7w7L95ORkNm3aFJZtBxLquFwuF82aNSM6Ojpk21BKVa0akRQyMjJITEwkNTUVEany7Z84cYLExMQq325lQhmXMYbDhw+TkZFBq1atQrINpVTVqxHDR263m7p164YlIZytRIS6deuGrXemlAqNGpEUAE0IYaC/c6VqnhqTFJRSSv18Z2VSmLkqk34Pf0mruz6l38NfMnNV5s9eZ0JCAgB79uzh8ssv/9nrU0qpcAhpUhCRYSKyWUS2ishd5cz/nYis9b0Wi0iXUMYDVkK4+4N1ZGblYYDMrDzu/mDdGUkMAE2aNGHGjBlnZF1KKVXVQpYURMQJPAsMBzoCo0WkY5lqO4CBxpjOwD+BF87Etq/87xKmL/8JgEKPlyv/u4QPV2UA8OicH8gr9JSqn1fo4YFPNgBwJKeAK/+7hHkb9wNw4MSpHUjduXMnnTp1AuDVV1/lsssuY9iwYaSlpfHXv/7Vrjd37lz69u1L9+7dueKKK8jOzgbgrrvuomPHjnTu3Jnbb7/9ND69UkqdvlCektob2GqM2Q4gIu8CI4CN/grGmMUl6n8HNAthPADsPVZ+I38kpzAk21u9ejWrVq0iNjaWdu3a8cc//pG4uDgefPBB5s2bR3x8PI888giPP/44EydO5MMPP+SHH35ARMjKygpJTEopVZFQJoWmwE8lyhnAeQHq/x74rLwZIjIeGA9Qv359Fi5cWGp+cnIyJ06csMsvXm3tqfunlSw3Sopl7/H8k7bROCmWEydOEF2mfhxw4kTghOHxeOz62dnZeL1eTpw4gdvtZsCAATgcDgoLC0lPT2fTpk1kZWWxYcMG+vbtC0BBQQG9e/dGRIiJiWHs2LEMHTqUYcOGlfpcp8rj8fys5YPhdrtP+vcIJDs7+5TqVwWNKTiRGBNEZlyRGFOwQpkUyjtf0ZRbUWQwVlLoX958Y8wL+IaW2rVrZwYNGlRq/qZNm4K+SOvO4R24+4N1pYaQ4qKd3Dm8w2lf6OVveBMTE0lISMDhcJCYmIjL5SIhIcFeb2xsLDExMcTFxXHRRRfxzjvvnLSu5cuXM3/+fN59911eeuklvvzyy9OKyR9XqC+qc7lcdOvWLej6CxcupOy/X7hpTMGJxJggMuOKxJiCFcqkkAE0L1FuBuwpW0lEOgMvAsONMYdDGA8Al3ZrCsDkzzezJyuPJilx3DG0nT29KvTp04ebb76ZrVu30rZtW3Jzc8nIyKBJkybk5ubyq1/9ij59+tC2bdsqi0kppSC0SWEZkCYirYBM4Crg6pIVRKQF8AEwxhjzYwhjKeXSbk2rNAmUVb9+fV599VVGjx5Nfr41lPXggw+SmJjIiBEjcLvdGGN44oknwhajUursFLKkYIwpEpGJwOeAE3jZGLNBRCb45k8F7gXqAs/5ro4tMsb0DFVMoeQ/eyg1NZX169cDMG7cOMaNG2fX+eSTT+z3Q4YMYdmyZSetZ+nSpaENVCmlAgjpDfGMMbOB2WWmTS3x/nrg+lDGoJRSKnhn5RXNSimlyqdJQSmllE2TglJKKZsmBaWUUjZNCkoppWw14nGcp2RyGuQcOHl6fAO4Y8tprTIrK4s33niDm2666ZSXXb16NXv27OFXv/rVaW1bKaXOpLOvp1BeQgg0PQjHjh3jueeeO61lV69ezezZsyuvqJRSVaBm9hReufjkaedcCr1vqHzZnMMw7drS0677NOAi9913H9u2baNr165ceOGFNGjQgGnTppGfn8/IkSOZNGkSH374Ic8++yxffPEF+/btY+DAgcybN497772XvLw8Fi1axN13382VV14Z/OdUSqkz7OzrKYTApEmTaNOmDatXr+bCCy9ky5YtLF26lNWrV7NixQq+/vprRo4cSaNGjXj22We54YYbmDRpEi1atOCBBx7gyiuvZPXq1ZoQlFJhVzN7CpXs2QcUX/dnLT937lzmzp1r3zk0OzubLVu2MGDAAJ5++mk6depEnz59GD169OnHqJRSIVIzk0IYGWO4++67ufHGG0+al5mZicPhYP/+/Xi9XhwO7agppSLL2dcqxTc4telBSEhIsJ+pMHToUF5++WX7BnmZmZkcOHCAoqIirrvuOt5++206dOjA448/DljPYAj1g3CUUipYZ19P4TRPOw2kbt269OvXj06dOjF8+HCuvvpq+6lqCQkJvPnmm0ydOpULLriACy64gK5du9KrVy8uvvhiBg8ezMMPP0zXrl31QLNSKuzOvqQQIm+//Xap8q233lqqfO+999rvExMT+eGHH+xyebfQVkqpcDj7ho+UUkpVSJOCUkopmyYFpZRSNk0KSimlbJoUlFJK2TQpKKWUsp21SeFg7kHGzRnHobxDZ2R9O3fupFOnTj9rHQsXLuTXv/71GYnnTEtNTeXQoTPzu1JKRa6zNilMXTuVlftX8vya58MdyhlhjMHr9YY7DKVUNVcjL167bs51Fc5bsX8FBmOXp22exrTN04iSKFZdu4qj7qPctvC2Usu8MuyVoLZbVFTE2LFjWbVqFenp6bz++utMmTKFWbNmkZeXx/nnn89///tfRIStW7cyYcIEDh48iNPpZPr06aXWtWzZMsaPH8/7779PYmIiV199NYcPH6ZXr17MmTOHFStWkJ2dzfDhwxk8eDBLlixh5syZPPPMM3z22WeICH/5y18YN24cCxcuZMqUKXzyyScATJw4kZ49ezJu3DhSU1MZO3Yss2bNorCwkOnTp9O+fXsOHz7M6NGjOXjwIL1798YYU95HVkrVMGddT+HceudSJ7YOUWLlQ5fTRR1XHW7reVslS1Zu8+bNjB8/nrVr15KUlMRzzz3HxIkTWbZsGevXrycvL89umH/3u99x8803s2bNGhYvXkzjxo3t9SxevJgJEybw0Ucf0bp1ayZNmsSQIUNYuXIlI0eOZPfu3aW2ee2117Jq1SqWL1/O6tWrWbNmDfPmzeMf//gHe/furTTuevXqsXLlSv7whz8wZcoUwLodeP/+/Vm1ahWXXHJJqW0qpWquGtlTqGzP/oElDzDjxxnEOGPI9+RzSdtLGNNxDAC1XbWD7hmU1bx5c/r16wfANddcw1NPPUWrVq149NFHyc3N5ciRI5xzzjkMGjSIzMxMRo4cCYDL5bLXsWnTJsaPH8/cuXNp0qQJAIsWLeLDDz8EYNiwYdSuXduu37JlS/r06WPXGz16NE6nk4YNG9KvXz+WLVtGUlJSwLgvu+wyAHr06MEHH3wAwNdff22/v/jii0ttUylVc9XIpFCZI+4jjGo3iivSr2D6j9PP2MFmETmpfNNNN7F8+XKaN2/O/fffj9vtDjgU07hxY9xuN6tWrbKTQqD68fHx9vuK6kVFRZU63uB2u0vNj42NBcDpdFJUVFTh51FK1Xxn3fARwJODn+SePvfQrk477ulzD08OfvKMrHf37t0sWbIEgHfeeYf+/fsD1vBMdnY2M2bMACApKYlmzZoxc+ZMAPLz88nNzQUgJSWFTz/9lL/97W8sXLgQgP79+zNt2jTAeojP0aNHy93+gAEDeO+99/B4PBw8eJDFixfTu3dvWrZsycaNG8nPz+fYsWPMnz+/0s8yYMAA3nrrLQA+++yzCreplKpZzsqeQqh06NCB1157jRtvvJG0tDT+8Ic/cPToUc4991xSU1Pp1auXXfeNN97gxhtv5N577yU6OrrUgeaGDRsya9Yshg8fzssvv8x9993H6NGjee+99xg4cCCNGzcmMTHRfmaD38iRI1myZAldunRBRHjggQdo1KgRAKNGjaJz586kpaXZT4ULxL/N7t27M3DgQFq0aHGGfktKqYhmjKlWr/T0dFPWxo0bT5pWlY4fPx7S9bvdblNYWGiMMWbx4sWmS5cuQS0X6riMOfXf/YIFC0ITyM+gMQUnEmMyJjLjisSYgOUmiDZWewrVwO7duxk1ahRer5eYmBj+97//hTskpVQNpUmhGkhLS2PVqlXhDkMpdRaoMQeajV5cVeX0d65UzVMjkoLL5eLw4cPaSFUhYwyHDx8udY2FUqr6qxHDR82aNSMjI4ODBw+GZftutzsiG8dQx+VyuWjWrFnI1q+Uqno1IilER0fTqlWrsG1/4cKFQZ3mWdUiNS6lVOQK6fCRiAwTkc0islVE7ipnvojIU775a0WkeyjjUUopFVjIkoKIOIFngeFAR2C0iHQsU204kOZ7jQdqxn2slVKqmgplT6E3sNUYs90YUwC8C4woU2cE8Lrv2orvgBQRaVx2RUoppapGKI8pNAV+KlHOAM4Lok5ToNT9nkVkPFZPAiBfRNaf2VB/tnpAJD6WLBLj0piCozEFLxLjisSY2gVTKZRJobxbbJY9ZzSYOhhjXgBeABCR5caYnj8/vDMnEmOCyIxLYwqOxhS8SIwrUmMKpl4oh48ygOYlys2APadRRymlVBUJZVJYBqSJSCsRiQGuAj4uU+dj4FrfWUh9gGPGmMofFaaUUiokQjZ8ZIwpEpGJwOeAE3jZGLNBRCb45k8FZgO/ArYCuUDFD1cu9kKIQv45IjEmiMy4NKbgaEzBi8S4qm1MoreGUEop5Vcj7n2klFLqzNCkoJRSylatkkJlt80IQzwvi8iBSLpuQkSai8gCEdkkIhtE5NYIiMklIktFZI0vpknhjslPRJwiskpEPgl3LH4islNE1onI6mBPIww1EUkRkRki8oPvb6tvmONp5/v9+F/HReRP4YzJF9effX/j60XkHREJ+50yReRWXzwbgvodBfN4tkh4YR2s3ga0BmKANUDHMMc0AOgOrA/376dETI2B7r73icCPEfB7EiDB9z4a+B7oE+7flS+e24C3gU/CHUuJmHYC9cIdR5mYXgOu972PAVLCHVOJ2JzAPqBlmONoCuwA4nzlacC4MMfUCVgP1MI6sWgekBZomerUUwjmthlVyhjzNXAknDGUZYzZa4xZ6Xt/AtiE9ccazpiMMSbbV4z2vcJ+hoOINAMuBl4MdyyRTESSsHaAXgIwxhQYY7LCGlRpvwC2GWN2hTsQrIY3TkSisBricF931QH4zhiTa4wpAr4CRgZaoDolhYpuiaEqICKpQDesPfOw8g3TrAYOAF8YY8IeE/Ak8FfAG+Y4yjLAXBFZ4bvFS7i1Bg4Cr/iG2l4UkfhwB1XCVcA74Q7CGJMJTAF2Y92q55gxZm54o2I9MEBE6opILaxLAJoHWqA6JYWgbomhLCKSALwP/MkYczzc8RhjPMaYrlhXrfcWkU7hjEdEfg0cMMasCGccFehnjOmOdRfhm0VkQJjjicIaJn3eGNMNyAHCfkwPwHdh7CXA9AiIpTbW6EUroAkQLyLXhDMmY8wm4BHgC2AO1rB7UaBlqlNS0FtiBElEorESwlvGmA/CHU9JvmGHhcCw8EZCP+ASEdmJNRQ5RETeDG9IFmPMHt/PA8CHWEOn4ZQBZJTo3c3AShKRYDiw0hizP9yBAL8EdhhjDhpjCoEPgPPDHBPGmJeMMd2NMQOwhru3BKpfnZJCMLfNOOuJiGCN/W4yxjwe7ngARKS+iKT43sdhfXl+CGdMxpi7jTHNjDGpWH9LXxpjwrpXByAi8SKS6H8PXIQ1BBA2xph9wE8i4r/L5i+AjWEMqaTRRMDQkc9uoI+I1PJ9D3+BdUwvrESkge9nC+AyKvl9VZvHcZoKbpsRzphE5B1gEFBPRDKA+4wxL4UzJqw94DHAOt8YPsDfjDGzwxcSjYHXfA9ecgDTjDERcwpohGkIfGi1KUQBbxtj5oQ3JAD+CLzl2yHbTnC3pAkp3xj5hcCN4Y4FwBjzvYjMAFZiDdGsIjJud/G+iNQFCoGbjTFHA1XW21wopZSyVafhI6WUUiGmSUEppZRNk4JSSimbJgWllFI2TQpKKaVsmhTUWU1EPGXutnnGrtQVkdRIuoOuUsGoNtcpKBUieb7bbyil0J6CUuXyPdPgEd9zIJaKSFvf9JYiMl9E1vp+tvBNbygiH/qeGbFGRPy3N3CKyP9897Kf67uiGxFpIyJzfDe9+0ZE2vumX+G79/0aEfk6LB9endU0KaizXVyZ4aMrS8w7bozpDTyDdUdVfO9fN8Z0Bt4CnvJNfwr4yhjTBeu+QP6r7dOAZ40x5wBZwG99018A/miM6QHcDjznm34vMNS3nkvO7EdVqnJ6RbM6q4lItjEmoZzpO4EhxpjtvhsM7jPG1BWRQ0BjY0yhb/peY0w9ETkINDPG5JdYRyrWbcLTfOU7sZ4l8STWrag3l9hkrDGmg4hMBdpgPaDlA2PM4RB8bKUqpMcUlKqYqeB9RXXKk1/ivQeIw+qhZ5V3LMMYM0FEzsN6+M9qEemqiUFVJR0+UqpiV5b4ucT3fjHWXVUBfgcs8r2fD/wB7AcKJVW0Ut/zLXaIyBW++iIiXXzv2xhjvjfG3AscopIHoih1pmlSUGe7sscUHi4xL1ZEvgduBf7sm3YLcJ2IrMW6G+2tvum3AoNFZB2wAjinku3+Dvi9iKzBOv7gf7TsZBFZ5zuV9Wush6IoVWX0mIJS5fAdU+hpjDkU7liUqkraU1BKKWXTnoJSSimb9hSUUkrZNCkopZSyaVJQSill06SglFLKpklBKaWU7f8BC8IPkU8y64YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for i in np.arange(len(recall_list)):\n",
    "    plt.plot(recall_list[i], linestyle=line_style[i], marker=markers[i], label=labels[i])\n",
    "plt.ylim(0, 1)\n",
    "plt.xlim(0, 9)\n",
    "plt.grid()\n",
    "plt.xlabel('Epoches')\n",
    "plt.ylabel('Training Recall')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_list = [[], [], []]\n",
    "recall_list = [[], [], []]\n",
    "\n",
    "for i in np.arange(10):\n",
    "    fn = \"cm_{0}.csv\".format(i)\n",
    "    cm = pd.read_csv(os.path.join(val_folder, fn), index_col=0, header=0)\n",
    "    true_val = np.sum(cm, axis=1)\n",
    "    det_val = np.sum(cm, axis=0)\n",
    "    for c_i in [0, 1, 2]:\n",
    "        acc = cm.iloc[c_i, c_i] / true_val[c_i]\n",
    "        recall = cm.iloc[c_i, c_i] / det_val[c_i]\n",
    "        acc_list[c_i].append(acc)\n",
    "        recall_list[c_i].append(recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for i in np.arange(len(acc_list)):\n",
    "    plt.plot(acc_list[i], linestyle=line_style[i], marker=markers[i], label=labels[i])\n",
    "plt.ylim(0, 1)\n",
    "plt.xlim(0, 9)\n",
    "plt.grid()\n",
    "plt.xlabel('Epoches')\n",
    "plt.ylabel('Validation Accuracy')\n",
    "plt.legend()\n",
    "plt.savefig('ValidationAccuracy.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for i in np.arange(len(recall_list)):\n",
    "    plt.plot(recall_list[i], linestyle=line_style[i], marker=markers[i], label=labels[i])\n",
    "plt.ylim(0, 1)\n",
    "plt.xlim(0, 9)\n",
    "plt.grid()\n",
    "plt.xlabel('Epoches')\n",
    "plt.ylabel('Validation Recall')\n",
    "plt.legend()\n",
    "plt.savefig('ValidationRecall.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loss_f = './results/run-tables_deeplab-resnet_experiment_4-tag-train_total_loss_iter.csv'\n",
    "val_mIou_f = './results/run-tables_deeplab-resnet_experiment_4-tag-val_mIoU.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loss = pd.read_csv(train_loss_f)\n",
    "val_mIou = pd.read_csv(val_mIou_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wall time</th>\n",
       "      <th>Step</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.592622e+09</td>\n",
       "      <td>18</td>\n",
       "      <td>0.031437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.592622e+09</td>\n",
       "      <td>42</td>\n",
       "      <td>0.018166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.592622e+09</td>\n",
       "      <td>45</td>\n",
       "      <td>0.017222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.592622e+09</td>\n",
       "      <td>71</td>\n",
       "      <td>0.012499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.592622e+09</td>\n",
       "      <td>94</td>\n",
       "      <td>0.010580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Wall time  Step     Value\n",
       "0  1.592622e+09    18  0.031437\n",
       "1  1.592622e+09    42  0.018166\n",
       "2  1.592622e+09    45  0.017222\n",
       "3  1.592622e+09    71  0.012499\n",
       "4  1.592622e+09    94  0.010580"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_loss.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wall time</th>\n",
       "      <th>Step</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.592623e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0.740700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.592625e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>0.752914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.592626e+09</td>\n",
       "      <td>2</td>\n",
       "      <td>0.775518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.592628e+09</td>\n",
       "      <td>3</td>\n",
       "      <td>0.769109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.592629e+09</td>\n",
       "      <td>4</td>\n",
       "      <td>0.796330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Wall time  Step     Value\n",
       "0  1.592623e+09     0  0.740700\n",
       "1  1.592625e+09     1  0.752914\n",
       "2  1.592626e+09     2  0.775518\n",
       "3  1.592628e+09     3  0.769109\n",
       "4  1.592629e+09     4  0.796330"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_mIou.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(train_loss['Step'], train_loss['Value'])\n",
    "plt.grid()\n",
    "plt.xlabel(\"Training Steps\")\n",
    "plt.ylabel(\"Training Loss/Step\")\n",
    "# plt.xlim([0, 20000])\n",
    "plt.savefig('TrainingLossStep.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(val_mIou['Step'], val_mIou['Value'])\n",
    "plt.grid()\n",
    "plt.xlabel(\"Training Epoches\")\n",
    "plt.ylabel(\"Valiation mIoU/Epoch\")\n",
    "plt.xlim([0, 9])\n",
    "plt.savefig('ValidationMIOUEpoch.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
